Application of Coupled Flow Network and Machine Learning Models for Data-Driven Forecasting of Reservoir Souring

Abstract

Summary Flow networks, in which flow paths are approximated by one-dimensional flow tubes, have recently appeared as a potentially powerful data-physics subsurface reservoir surrogate modelling technique. Such models are fast, because the number of grid cells is considerably reduced when compared to conventional numerical reservoir model grids. Flow network models can be generated and trained directly from data rather than constructed through reduction of high-fidelity models and can be simulated using existing industry-standard reservoir simulators. The first objective of this paper is to summarize recent extensions of an open-source framework for creating and training flow networks, called FlowNet, and to demonstrate its application to a complex oil field case. Despite their flexibility, the application of flow networks will be limited to physics that can be simulated with the available simulators. Prediction of phenomena with poorly understood underlying physics, chemistry, or, as in the case of reservoir souring, microbial ecology, may require a more data-driven approach. The second objective of this paper is therefore to investigate the usefulness of an extension of the FlowNet methodology with a machine learning proxy that can be used to produce predictions of H2S in the absence of a physics-based simulator module. The proxy is trained on historical liquid volume rates, seawater fractions, and H2S production data from a complex producing field, and then used to generate predictions of H2S production using FlowNet-based predictions of these same features as input. We introduce the main characteristics of the field case, including a brief review of current understanding of reservoir souring taking place in the field. Several experiments are presented in which the source, type, and length of the training data time series are varied. Results indicate that, given a sufficient number of training data points, FlowNet is able to produce reliable predictions of conventional oil field quantities. The experiments performed with the machine learning proxy as an add-on, suggest that, at least for certain classes of production wells, useful predictions of H2S production can be obtained much faster and at much lower computational cost and complexity than would be possible with high-fidelity models. Finally, we discuss some current limitations and options to address them.